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{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "eefe7571", "metadata": {}, "outputs": [], "source": [ "# show differentiation in Tensorflow" ] }, { "cell_type": "code", "execution_count": null, "id": "a9d7c185", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": null, "id": "584384f1", "metadata": {}, "outputs": [], "source": [ "# Define a function to differentiate\n", "def f(x):\n", " return x ** 2 + 2 * x + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "70430402", "metadata": {}, "outputs": [], "source": [ "# Create a TensorFlow variable\n", "x = tf.Variable(2.0)" ] }, { "cell_type": "code", "execution_count": null, "id": "45ea0a33", "metadata": {}, "outputs": [], "source": [ "# Use tf.GradientTape to record the gradients\n", "with tf.GradientTape() as tape:\n", " y = f(x)" ] }, { "cell_type": "code", "execution_count": null, "id": "f6b1ff27", "metadata": {}, "outputs": [], "source": [ "# Calculate the gradient of y with respect to x\n", "dy_dx = tape.gradient(y, x)" ] }, { "cell_type": "code", "execution_count": null, "id": "4f581817", "metadata": {}, "outputs": [], "source": [ "# Print the result\n", "print(dy_dx)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5}
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